Targeting and data collection techniques provide advertisers and other marketing organizations with market segment data related to advertising viewers, including, for example, computer users who view advertising on the World Wide Web (Web) or Internet. For advertising viewers such as Internet users, the available information related to each user depends, for example, on his or her historical Web behavior and, for example, on his or her origin environment, such as the user's computing platform, service provider, country, time of day, etc. A “market segment” or “segment” is a subset, or partial portion of a group that can be characterized in some way; a segment may also be a data object describing such a group.
Advertisers and other marketing organizations may create segment definitions to define groups of potential marketing targets (e.g., users) and direct advertising to those groups, such as groups of users on the Internet. “Data publishers” (or “data sellers”) may sell information concerning targets or people, such as Internet users, and their behaviors, which advertisers and other marketing organizations may use to create, for example, behavioral segment definitions. An Internet user may access a Web site of a data publisher, such as a bicycling interest Web site, for example, and be identified as a user “interested in bicycling.” Other attributes, such as time and location of the person's access, may also be identified. Data publishers may sell the identifying information about users who access their sites and receive income from sales based on this information's use.
User identification (ID) data from data publishers can be used to create segment definitions. In general, segment definitions may be characterized by specific values for available properties. For example, segment definitions might exist for categories such as “Gender”, “Age” and “Nationality” and one segment combination might be defined with three properties as, “Male, 35-40, European.” Once identified (e.g., from information from a data publisher (data seller)), a user who fits the characteristics of “Male, 35-40, European” can be grouped into and/or associated with this segment combination. An advertisement can be exposed to (or placed) with users identified with the segment combination, and data can be collected to determine how the users identified with that segment respond. Behavioral segment definitions for “Shopping Interest”, “Running Interest” and “Web surfing interest” can be defined and Behavioral attributes, such as “likes to shop”, “intensely likes running” or “Web surfs in the evening” can also be included in segment combinations. Segment combinations can have attributes that are purely behavioral, purely non-behavioral or a mixture of behavioral and non-behavioral.
The efficacy of a given advertisement depends on the match between the content of the advertisement (advertising content) and the market segment to which the content is exposed. In practice, a numeric “conversion ratio” value describes the efficiency or “success” relationship between the advertising content and target segment. A high conversion ratio value can show, for example, by various measures or various methods of determining or collecting such data, that a given advertisement or advertising campaign (group of advertisements) is well received by a given target segment.
It is perceived within the advertising and marketing industries that, in general, better and more accurate segment targeting capabilities could improve conversion ratios. High conversion ratios for advertisements, on the Internet and in other advertising venues, such as, e.g., print, outdoor, direct are desirable. Identification, for example, of a large user group with a high response rate to advertising and with members who respond in stable and predictable manners over time is desirable.
With the development of the Internet advertising market, information about the people most likely to visit a website and information about the people most likely to purchase a product from visiting a website is increasingly more valuable. These people may be classified into modeled audience extensions, which defines segments that define users most likely to take certain actions. More accurate and efficient identification of modeled audience extensions can lead to more conversions and better return on investment for advertising money spent.
However, it is difficult to collect comprehensive, meaningful, and useful attribute information for a large number of users with a large number of potential attributes. For example, attribute information may be collected for users while browsing the Internet, in which the number of tracked attributes may be in the millions. A user may have attributes corresponding to visiting one or more websites, the time and date of visiting websites, and whether orders were placed on websites. As a result, it is frequently difficult to ascertain values for all or even a substantial number of the attributes, because users may not have been in situations in which the values could be collected. Similarly, tracking online behavior may yield little or no information about offline information, such as the purchasing habits or attitudes of users when conducting offline transactions.
In general, there is a need for improved techniques for scaling a panel, in the advertising and marketing fields in general and, in particular, with regard to Internet advertising.